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Toward the Rational Design of More Efficient Mo<sub>2</sub>C Catalysts for Hydrodeoxygenation–Mechanism and Descriptor Identification

Raghavendra Meena, Johannes H. Bitter, Han Zuilhof, Guanna Li

2023ACS Catalysis21 citationsDOIOpen Access PDF

Abstract

High Resolution Image Download MS PowerPoint Slide Viable alternatives to scarce and expensive noble-metal-based catalysts are transition-metal carbides such as Mo and W carbides. It has been shown that these are active and selective catalysts in the hydrodeoxygenation of renewable lipid-based feedstocks. However, the reaction mechanism and the structure–activity relationship of these transition-metal carbides have not yet been fully clarified. In this work, the reaction mechanism of butyric acid hydrodeoxygenation (HDO) over molybdenum carbide (Mo 2 C) has been studied comprehensively by means of density functional theory coupled with microkinetic modeling. We identified the rate-determining step to be butanol dissociation: C 4 H 9 *OH + * → C 4 H 9 * + *OH. Then we further explored the possibility to facilitate this step upon heteroatom doping and found that Zr- and Nb-doped Mo 2 C are the most promising catalysts with enhanced HDO catalytic activity. Linear-scaling relationships were established between the electronic and geometrical descriptors of the dopants and the catalytic performance of various doped Mo 2 C catalysts. It was demonstrated that descriptors such as dopants’ d-band filling and atomic radius play key roles in governing the catalytic activity. This fundamental understanding delivers practical strategies for the rational design of Mo 2 C-based transition-metal carbide catalysts with improved HDO performance.

Topics & Concepts

HydrodeoxygenationCatalysisHeteroatomDopantCarbideDensity functional theoryRational designMaterials scienceTransition metalChemistryComputational chemistryDopingChemical engineeringChemical physicsNanotechnologyOrganic chemistryAlkylSelectivityOptoelectronicsEngineeringCatalysis and Hydrodesulfurization StudiesElectrocatalysts for Energy ConversionMachine Learning in Materials Science
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